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// RUN: mlir-opt --transform-interpreter %s | FileCheck %s
// CHECK-LABEL: func.func @generalize_unary
func.func @generalize_unary(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK-NOT: linalg.elemwise_unary
// CHECK: linalg.generic
%0 = linalg.elemwise_unary ins(%arg0 : tensor<?x?xf32>)
outs(%arg1: tensor<?x?xf32>) -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
// CHECK-LABEL: func @map_no_inputs(
func.func @map_no_inputs(%input: tensor<16x32x64xf32>,
%init: tensor<16x64xf32>) -> tensor<16x64xf32> {
// CHECK-NOT: linalg.map
// CHECK: linalg.generic
%reduce = linalg.reduce
ins(%input:tensor<16x32x64xf32>)
outs(%init:tensor<16x64xf32>)
dimensions = [1]
(%in: f32, %out: f32) {
%0 = arith.addf %out, %in: f32
linalg.yield %0: f32
}
func.return %reduce : tensor<16x64xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = transform.structured.generalize %0 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
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